{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用import导入必要的工具包，用于做特征工程\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "from scipy import sparse\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from nltk.metrics import distance as distance\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"diabetes.csv\")\n",
    "train.head()\n",
    "#读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()\n",
    "#查看每个特征的最大、最小值、以及中位数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有些列的最小值为零，对于这特征来说，零是无意义的这些列分别有：\n",
    "Glucose(血浆葡萄糖浓度)\n",
    "BloodPressure（舒张压）\n",
    "SkinThickness（三头肌皮肤褶层厚度）\n",
    "Insulin（血清胰岛素含量）\n",
    "BMI（体重）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose            5\n",
      "BloodPressure     35\n",
      "SkinThickness    227\n",
      "Insulin          374\n",
      "BMI               11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#看一些这些特征的缺失值有多少\n",
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print ((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分别查看这几列特征的数据分布情况\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Glucose.values, bins=50,kde=True)\n",
    "plt.xlabel('Glucose',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.BloodPressure.values, bins=40,kde=True)\n",
    "plt.xlabel('BloodPressure',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.SkinThickness.values, bins=30,kde=True)\n",
    "plt.xlabel('SkinThickness',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAENCAYAAAARyyJwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XmcXWWd5/HPt/bKXkkqe0gCqSAJGiBpcOnG1ggCKrG7oQn6UhzpZtqRcZ8emB4dhmlHUWzUF7QtAm1k1ECjtmlFggIuiAaKLRBCoLJX1iJ7pVL7b/64p5JLUXXq1pZbKb7v1+u+6pznPOe5z1MX8q2z3OcoIjAzM+tOQb47YGZmQ5uDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwsVVG+OzAQJk6cGLNnz853N8zMTipPPvnkKxFR2VO9YREUs2fPprq6Ot/dMDM7qUjakks9n3oyM7NUDgozM0vloDAzs1QOCjMzS+WgMDOzVA4KMzNL5aAwM7NUOQWFpIskrZdUI+m6LraXSron2b5a0uykfIKkRyTVS7o1q/5oSc9kvV6R9PVk20ck1WVt+5uBGaqZmfVFj1+4k1QI3AZcANQCT0haGREvZFW7GtgfEXMlLQNuAq4AGoHPA2cmLwAi4jBwVtZ7PAn8OKu9eyLi2j6PyszMBkwu38w+F6iJiI0AklYAS4HsoFgK3JAs3wfcKkkRcQR4VNLc7hqXVAVMAn7X++4PfT9YvbXHOh8475QT0BMzs77J5dTTdGBb1nptUtZlnYhoBQ4CE3Lsw5VkjiAiq+yvJK2RdJ+kmTm2Y2ZmgyCXoFAXZdGHOt1ZBvwwa/0/gNkR8SbgV8DyLjslXSOpWlJ1XV1djm9lZma9lUtQ1ALZf9XPAHZ0V0dSETAW2NdTw5IWAkUR8WRHWUTsjYimZPU7wKKu9o2I2yNicUQsrqzscfJDMzPro1yC4gmgStIcSSVkjgBWdqqzErgqWb4MeLjTqaTuXMmrjyaQNDVr9VJgXQ7tmJnZIOnxYnZEtEq6FlgFFAJ3RcRaSTcC1RGxErgTuFtSDZkjiWUd+0vaDIwBSiS9H7gw646pvwYu6fSWn5B0KdCatPWRfozPzMz6KafnUUTE/cD9ncq+kLXcCFzezb6zU9o9tYuy64Hrc+mXmZkNPn8z28zMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwsVU5BIekiSesl1Ui6rovtpZLuSbavljQ7KZ8g6RFJ9ZJu7bTPr5M2n0lek9LaMjOz/OgxKCQVArcBFwPzgSslze9U7Wpgf0TMBW4BbkrKG4HPA5/rpvkPRsRZyWtPD22ZmVke5HJEcS5QExEbI6IZWAEs7VRnKbA8Wb4PWCJJEXEkIh4lExi56rKtXuxvZmYDKJegmA5sy1qvTcq6rBMRrcBBYEIObf9rctrp81lh0Ne2zMxsEOQSFF39NR99qNPZByPijcCfJa8P9aYtSddIqpZUXVdX18NbmZlZX+USFLXAzKz1GcCO7upIKgLGAvvSGo2I7cnPw8APyJziyrmtiLg9IhZHxOLKysochmFmZn2RS1A8AVRJmiOpBFgGrOxUZyVwVbJ8GfBwRHR7RCGpSNLEZLkYeC/wfF/aMjOzwVXUU4WIaJV0LbAKKATuioi1km4EqiNiJXAncLekGjJ//S/r2F/SZmAMUCLp/cCFwBZgVRIShcCvgO8ku3TblpmZnXg9BgVARNwP3N+p7AtZy43A5d3sO7ubZhd1U7/btszM7MTzN7PNzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLFVOQSHpIknrJdVIuq6L7aWS7km2r5Y0OymfIOkRSfWSbs2qP0LSzyW9KGmtpC9nbfuIpDpJzySvv+n/MM3MrK96DApJhcBtwMXAfOBKSfM7Vbsa2B8Rc4FbgJuS8kbg88Dnumj65oh4A3A28DZJF2dtuycizkped/RqRGZmNqByOaI4F6iJiI0R0QysAJZ2qrMUWJ4s3wcskaSIOBIRj5IJjGMioiEiHkmWm4GngBn9GIeZmQ2SXIJiOrAta702KeuyTkS0AgeBCbl0QNI44H3AQ1nFfyVpjaT7JM3sZr9rJFVLqq6rq8vlrczMrA9yCQp1URZ9qPPahqUi4IfANyNiY1L8H8DsiHgT8CuOH6m8uvGI2yNicUQsrqys7OmtzMysj3IJilog+6/6GcCO7uok//iPBfbl0PbtwMsR8fWOgojYGxFNyep3gEU5tGNmZoMkl6B4AqiSNEdSCbAMWNmpzkrgqmT5MuDhiEg9opD0j2QC5VOdyqdmrV4KrMuhj2ZmNkiKeqoQEa2SrgVWAYXAXRGxVtKNQHVErATuBO6WVEPmSGJZx/6SNgNjgBJJ7wcuBA4B/wC8CDwlCeDW5A6nT0i6FGhN2vrIAI3VzMz6oMegAIiI+4H7O5V9IWu5Ebi8m31nd9NsV9c1iIjrgetz6ZeZmQ0+fzPbzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMzCxVTkEh6SJJ6yXVSLqui+2lku5Jtq+WNDspnyDpEUn1km7ttM8iSc8l+3xTkpLy8ZJ+Kenl5GdF/4dpZmZ91WNQSCoEbgMuBuYDV0qa36na1cD+iJgL3ALclJQ3Ap8HPtdF098CrgGqktdFSfl1wEMRUQU8lKybmVme5HJEcS5QExEbI6IZWAEs7VRnKbA8Wb4PWCJJEXEkIh4lExjHSJoKjImIP0REAN8D3t9FW8uzys3MLA9yCYrpwLas9dqkrMs6EdEKHAQm9NBmbTdtTo6InUlbO4FJOfTRzMwGSS5BoS7Kog91+lP/tQ1I10iqllRdV1fXm13NzKwXcgmKWmBm1voMYEd3dSQVAWOBfT20OaObNncnp6Y6TlHt6aqBiLg9IhZHxOLKysochmFmZn2RS1A8AVRJmiOpBFgGrOxUZyVwVbJ8GfBwcu2hS8kppcOS3pzc7fRh4KddtHVVVrmZmeVBUU8VIqJV0rXAKqAQuCsi1kq6EaiOiJXAncDdkmrIHEks69hf0mZgDFAi6f3AhRHxAvAx4LtAOfCL5AXwZeBeSVcDW4HLB2KgZmbWNz0GBUBE3A/c36nsC1nLjXTzD3pEzO6mvBo4s4vyvcCSXPplZmaDz9/MNjOzVA4KMzNL5aAwM7NUDgozM0vloDAzs1QOCjMzS+WgMDOzVA4KMzNL5aAwM7NUDgozM0vloDAzs1QOCjMzS+WgMDOzVA4KMzNL5aAwM7NUDgozM0vloDAzs1QOCjMzS+WgMDOzVA4KMzNLlVNQSLpI0npJNZKu62J7qaR7ku2rJc3O2nZ9Ur5e0ruTstMlPZP1OiTpU8m2GyRtz9p2ycAM9cRraWtnf0NzvrthZtYvRT1VkFQI3AZcANQCT0haGREvZFW7GtgfEXMlLQNuAq6QNB9YBiwApgG/kjQvItYDZ2W1vx34SVZ7t0TEzf0fXn7dvGo93/7tRs6eOY53L5jCmPLifHfJzKzXcjmiOBeoiYiNEdEMrACWdqqzFFieLN8HLJGkpHxFRDRFxCagJmkv2xJgQ0Rs6esghqIjTa38YPVWJo4q4bntB/mnX77E1r1H8t0tM7NeyyUopgPbstZrk7Iu60REK3AQmJDjvsuAH3Yqu1bSGkl3SarIoY9Dzo+equVwUyuXLZrJp941j8ICsXrTvnx3y8ys13IJCnVRFjnWSd1XUglwKfBvWdu/BZxG5tTUTuBrXXZKukZStaTqurq67nufB+3twXcf28zCGWOZWVHO+JElzJ86hnW7DtHa3p7v7pmZ9UouQVELzMxanwHs6K6OpCJgLLAvh30vBp6KiN0dBRGxOyLaIqId+A6vPVXVUe/2iFgcEYsrKytzGMaJ89uX69hYd4T/9LY5ZM7AwYJpY2hsaWdjnU8/mdnJJZegeAKokjQnOQJYBqzsVGclcFWyfBnwcEREUr4suStqDlAFPJ6135V0Ou0kaWrW6l8Az+c6mKFi+WObqRxdyiVvPD6U0yaNorSogLU7DuaxZ2ZmvddjUCTXHK4FVgHrgHsjYq2kGyVdmlS7E5ggqQb4DHBdsu9a4F7gBeAB4OMR0QYgaQSZO6l+3OktvyLpOUlrgHcAn+7nGE+o5tZ2fl+zl6ULp1FSdPzXW1xYwOlTRvPCjkO0R+czd2ZmQ1ePt8cCRMT9wP2dyr6QtdwIXN7Nvl8EvthFeQOZC96dyz+US5+Gqhd3HaK5rZ2zT3ntNfgF08aypvYgm185wqmVo/LQOzOz3vM3swfYs7WZU0tvmjH2NdtOnzyaogKxdsehE90tM7M+c1AMsDXbDjB+ZAkzKspfs62kqIC5k0bx0u7DeeiZmVnfOCgG2Jrag7xpxthjdzt1NmvCSPYeaaahqfUE98zMrG8cFAOoobmVl/cc5k0zxnVbZ2ZypLFtf8OJ6paZWb84KAbQ89sP0R6wsIvrEx2mV5QjYNv+oyeuY2Zm/eCgGEBrag8ApB5RlBYVMnlMGdv2+YjCzE4ODooB9GztQaaNLaNydGlqvZnjy6ndf9TfpzCzk4KDYgCtqT2QejTRYWbFCI62tLG33s+qMLOhz0ExQA40NLNlbwNvmtn99YkOM8aPAHxB28xODg6KAfLc9swX7RbmcEQxaXQppUUFvk5hZicFB8UAqdlTD8C8yaN7rFsgMb0ic53CzGyoc1AMkA119YwpK2LiqJKc6s+sGMHOg0dpafPzKcxsaHNQDJCNdZmJ/rr7RnZnMytG0B6w44CPKsxsaHNQDJANdfWc1osZYacn39De7qAwsyHOQTEA6pta2X2oiVMrR+a8z5iyIkaXFrHd1ynMbIhzUAyAjXWZC9m9OaKQxLRx5T6iMLMhz0ExADqeg31aL44oIHP6qe5wEw3NnknWzIYuB8UA2FBXT2GBOGXCiF7tN31cOQG84AcZmdkQ5qAYABvrjjCzopzSosJe7Td9XOaC9prkqXhmZkORg2IA9PaOpw5jyosZXVbE89sdFGY2dDko+qmtPdj0ypFe3fGUbfq48mPTf5iZDUU5BYWkiyStl1Qj6boutpdKuifZvlrS7Kxt1yfl6yW9O6t8s6TnJD0jqTqrfLykX0p6OflZ0b8hDq4dB47S1NrepyMKyARFTV09R/xoVDMbonoMCkmFwG3AxcB84EpJ8ztVuxrYHxFzgVuAm5J95wPLgAXARcA/J+11eEdEnBURi7PKrgMeiogq4KFkfcja0HFr7KS+B0UEvLDTF7TNbGjK5YjiXKAmIjZGRDOwAljaqc5SYHmyfB+wRJm5LJYCKyKiKSI2ATVJe2my21oOvD+HPubNhuTW2FMn9u3U07QKX9A2s6Etl6CYDmzLWq9NyrqsExGtwEFgQg/7BvCgpCclXZNVZ3JE7Eza2glM6qpTkq6RVC2puq6uLodhDI4NdfWMG1HM+JG5TQbY2ZiyYqaMKePZbQcGuGdmZgMjl6Doapa7zs/w7K5O2r5vi4hzyJzS+rik83Poy/FGIm6PiMURsbiysrI3uw6ojXX1nDpxZM6TAXZl0awKntyyfwB7ZWY2cHIJilpgZtb6DGBHd3UkFQFjgX1p+0ZEx889wE84fkpqt6SpSVtTgT25D+fE21B3pM8XsjssmlXB9gNH2XWwcYB6ZWY2cHIJiieAKklzJJWQuTi9slOdlcBVyfJlwMMREUn5suSuqDlAFfC4pJGSRgNIGglcCDzfRVtXAT/t29AG36HGFuoON3FqP4Ni8ezMjV3VW/YNRLfMzAZUj0GRXHO4FlgFrAPujYi1km6UdGlS7U5ggqQa4DMkdypFxFrgXuAF4AHg4xHRBkwGHpX0LPA48POIeCBp68vABZJeBi5I1oekvs7x1NkZU8dQXlxI9WaffjKzoacol0oRcT9wf6eyL2QtNwKXd7PvF4EvdirbCCzspv5eYEku/cq3jllj+3tEUVxYwMKZY32dwsyGJH8zux821NVTVCBm9XIywK4snjWeF3Ye8hfvzGzIcVD0w8a6I5wyfgTFhf3/NS6aXUFbe/g2WTMbchwU/bChrr7fp506nHNKBRJU+/STmQ0xDoo+amsPNr/SwGmT+nchu8PY8mLmTRrtoDCzIcdB0Ue1+xtobmvntIkDc0QBmdNPT2/ZT2tb+4C1aWbWXw6KPjp2a+wAHVEAnF9VyeGmVh7f5O9TmNnQ4aDoo45ZY08dwCOK8+dNpLSogAdf2D1gbZqZ9ZeDoo821NUzfmQJFX2cDLArI0qKOH9eJQ+u3UXmi+1mZvnnoOijDXVH+jy1eJoL509mx8FGnt/u51OY2dDgoOijjX18TnZPlpwxmQLBqrW7BrxtM7O+cFD0wcGGFl6pb+7zc7LTjB9ZwrlzxvPgCw4KMxsaHBR98NKewwDMmzx6UNq/cP4UXtpdz6ZXjgxK+2ZmveGg6IOXdmeComrywJ96Anj3mVOQYMXjWwelfTOz3nBQ9MFLuw4zsqSQ6ePKB6X96ePKWbpwGt/7wxbqDjcNynuYmeXKQdEHL+2up2ry6H49/rQnn1hSRVNrG9/+zYZBew8zs1w4KPrgpd2HOX2Qrk90OLVyFH9x9gzu/uMW9hzyI1LNLH8cFL30Sn0Te480M2/K4AYFwCeWzKW1Pfj6Qy8P+nuZmXXHQdFLHRey5w3ShexssyaM5MNvmcUPVm/ltkdqBv39zMy6ktOjUO24l3ZlgmKwTz11+J/vmc/+I818ddV6CiT+7u2nDuq1ETOzzhwUvfTSnnrGlhdTObr0hLxfYYG4+fKFtAXc9MCL/OTpWj70ltm88w2TmDqmjIICh4aZDa6cgkLSRcA3gELgjoj4cqftpcD3gEXAXuCKiNicbLseuBpoAz4REaskzUzqTwHagdsj4htJ/RuAvwXqkub/R0Tc348xDqiXdmUuZJ/Iv+qLCgu45a8X8mdVE1n+2GY+/+/P83mgtKiAGRXlTBhVSkNTKxUjSxg/soQpY8qYPq6cok6PaP3AeaecsD6b2fDRY1BIKgRuAy4AaoEnJK2MiBeyql0N7I+IuZKWATcBV0iaDywDFgDTgF9Jmge0Ap+NiKckjQaelPTLrDZviYibB2qQAyUieGn3Yd63cNoJfd8frD7+xbsPnHsK2w8cZfuBo+ytb2Z/QzN7DjVR39TCul2HaWvPzDpbVCBmjh/BgmljOHPaWMaUF5/QPpvZ8JHLEcW5QE1EbASQtAJYCmQHxVLghmT5PuBWZf7kXgqsiIgmYJOkGuDciPgDsBMgIg5LWgdM79TmkLP7UBOHGls5/QTc8dQdScyoGMGMihGv2dYewaGjLew4cJTNext4ec9hfrZmJz9fs5OqyaN4afdh5k4aRUE3R0M+4jCzruQSFNOBbVnrtcB53dWJiFZJB4EJSfkfO+07PXtHSbOBs4HVWcXXSvowUE3myGNIPEj6+B1P+QuKNAUS40aUMG5ECfOnjQWmsudQI8/WHqR68z6++9hmJo0uZckZk1kwbUy3gWFmli2X22O7+tek81N1uquTuq+kUcCPgE9FRMcDGL4FnAacReao42tddkq6RlK1pOq6urquqgy49buGdlB0ZdKYMi6YP5n/dtHpXL5oBgH88PGt/PMjNazfdcgPSDKzHuUSFLXAzKz1GcCO7upIKgLGAvvS9pVUTCYkvh8RP+6oEBG7I6ItItqB75A59fUaEXF7RCyOiMWVlZU5DKP/nq09wPRx5YwfwKfanShFBQWcfUoFn1xSxeWLZtDY2s7yP2zh27/dyMZX6vPdPTMbwnIJiieAKklzJJWQuTi9slOdlcBVyfJlwMOR+VN1JbBMUqmkOUAV8Hhy/eJOYF1E/FN2Q5KmZq3+BfB8bwc1WJ7eeoCzThmX7270S4HE2adU8Ol3zWPpWdM40NDMHb/bxPLHNrNup5+qZ2av1WNQREQrcC2wClgH3BsRayXdKOnSpNqdwITkYvVngOuSfdcC95K5SP0A8PGIaAPeBnwIeKekZ5LXJUlbX5H0nKQ1wDuATw/UYPtjz6FGth84ytkzT+6g6FBYIM6bM4HPXng6Fy2YwpZ9R7jkm7/jM/c+Q+3+hnx3z8yGEA2Hc9SLFy+O6urqQX2PVWt38Z/vfpIffeytLJpVkfN+2be2DmVHm9vYU9/Iv/5+MwR8+C2z+Pg75lJxEp5mM7PcSHoyIhb3VM9zPeXo6a0HKC4UC6aNyXdXBkV5SSHXX3wGv/7cn7P0rGnc9ftNnP/VR7jtkRoamlvz3T0zyyMHRY6e2rqf+dPGUlZcmO+uDKpp48r56uUL+cUnz+e8OeP56qr1nP+VX7P8sc00tbblu3tmlgcOihy0trWzpvbAsLk+kYvTp4zmjqv+hB997C2cVjmS/7VyLe+8+TfcW72N1rb2fHfPzE4gTwqYgxd3HaaxpZ2zT/I7nnrS3fWUSxdOY/60MTy4djd/f98a7vjdRm543wLeOnfiCe6hmeWDgyIHT287AMA5p+R+EXs4kUTVpNHMrRzF2h2H+MXzO/nAHas5c/pYLjlzCuNGHL/g7WlAzIYfB0UOnt66n4mjSphRUZ7vruSVJM6cPpbTp4zmty/X8Zv1dazfdYi3z5vEn1VNpLjQZzLNhiMHRQ8igie37OesmeP8wKBEcWEBS94wmXNOqeD+53byq3W7eXbbAS5bNCPfXTOzQeA/AXtQs6eeLXsbePu8EzNNyMmkYkQJHzxvFh9562ya29r5l99s4Eu/WEdji++OMhtOHBQ9WLV2FwAXzJ+S554MXfMmj+aTS6pYNKuCb/9mI+/55u94auuQmPDXzAaAg6IHq9bu5qyZ45gytizfXRnSyooL+ctzZvC9j57L0eY2LvvWY3zpfh9dmA0HDooU2w8c5bntB3n3Ah9N5Or8eZWs+vT5XPEnp/Dt327kEh9dmJ30HBQpVj2fOe307gWT89yTk8vosmK+9Jdv5O6rz6WppZ3LvvUY/9dHF2YnLd/1lGLV2l1UTRrFqZWj8t2Vk0bnL+1d/adzeOD5Xdz+2438+Kla3vPGqdxw6QLfQWZ2EvERRTf2HWnmic37uOhMn3bqj7LiQt5/9nQ++rY5RMDyP2zhQ3c+zrPJlxjNbOhzUHTj+3/cQnvAJW+c2nNl69HcSaP45LuqeO+bpvL8joMsve33XHn7H3n4xd2eO8psiPOppy4cbGjh9t9t5F1nTOaMqcNzWvF8KCoo4K2nTeRLf/lGfvj4Vu58dBMf/W41E0eVcunCaSw5YxKLZlUM+xl6zU42Doou3PHoRg43tvKZC+bluyvD0uiyYq45/zSueutsHnmxjp88Xcvdf9zMXb/fRGlRAQtnjGP+tDGcMXU0M8ePYGbFCKaOLaPIU4SY5YWDopN9R5q569FNvOeNU5k/TB9SlG+dL3i/fd4k3jxnApv2HmHDnnq27T/K09v209J2/OmLBYKx5cWMG1HCuPJi/qxqItPGlTO9ojzzc1y5j0TMBomDopOvPbiehpY2PvWuqnx35XWltLiQN0wZwxumZMK5PYL9R5rZ39DCgYZm9jU0c6Chhf1Hmtn4yhGerT1Ae6en+E4cVcKciSOT1yjmTBzJqZUjOWX8CIeIWT84KLL8y2828P3VW/no2+ZQNXl0vrvzulYgMWFUKRNGlXa5va09OHS0hQNHM0Fy4GgL+440U3e4iXU7D1PfdPzxrQLGjSimcnQpk0aXJT9L+difn/aqKdLNrGs5BYWki4BvAIXAHRHx5U7bS4HvAYuAvcAVEbE52XY9cDXQBnwiIlaltSlpDrACGA88BXwoIpr7N8ye/fDxrXz5Fy/yvoXT+J/vOWOw3876qbBAVIwsoWJkCTDyNdsbW9p4pb6JV+qbk59N1B1uYmPdXlqTQ5Fv/3YjE0eVMnfSSOZOGsXcylHMnjiSKWPLmDy6jHEjiv19DzNyCApJhcBtwAVALfCEpJUR8UJWtauB/RExV9Iy4CbgCknzgWXAAmAa8CtJHVeIu2vzJuCWiFgh6V+Str81EIPtSs2eer7ywIs8+MJu3j6vkq9dvpCCAv/jcLIrKy5kRsUIZlSMeFV5ewQHGlrYc7iRGRXl1Oypp2ZPPSuf2cGhxtZX1S0pKmDymFLGjyhhdFkxo8uKklfxq36OKStiVGkx5SUFlBYVUlac+Vna8bOogPYImlvbaW5tpyl5HWps4eDRFg4lr4NZr0NHW1+1vu9IM4UFoqhAFBaI4sICigpFWXEhZUUFnDl97LE+jcnuW3lHH4sZVVpEWXGmP/5v3HojlyOKc4GaiNgIIGkFsBTIDoqlwA3J8n3Arcr8KbYUWBERTcAmSTVJe3TVpqR1wDuBDyR1liftDkpQfPf3m/g/P19HeXEhn71gHn97/qmUFPnOmuGsQGL8yBLGjyx51dP4IoK6+ia27Wtg18Emdh9q5JH1ezh0tIWG5jb2HWmmsbWdppY2GlvaaR6k734UF4ry4kLKigspLymkvLiQSaNLmVFRTntAW3s7LW1BW3vQ0tZOY0sbBxta2HN4D4caW2hsya1fmbARxQWZwCkqzIRiR9CVFRUeC5XS4uPhV1ZccKy8rHN5USGlybbCJNAKJArE8eUkoCKCyLrGFAHB8bLoqJO1nddsz+xzbP+s9qTkhY6vJz8h06cCJX0sSPqojmUly8frSMfbz+5rl/3sZvux/iblmZ5kHgjW0Z8CCRVw7PdWoMx7d2zr+MwPN7ay53ATdYcbOWPqGGZNeO1R9UDKJSimA9uy1muB87qrExGtkg4CE5LyP3bad3qy3FWbE4ADEdFzHZRXAAAGlUlEQVTaRf0Bd86sCj705ln813fO7fZcuA1f3T0jHDJHJBef2f2XLdvag6bWTGg0trTR1NpOS1s7rW1Ba/KP+bGfbe0USBQV6thRQVFBwbEwKCsuoLw4Ewr9vQW4tb2dpqRPjS3tHG1pS/qXWW9NAqa1rZ2W9kzfOvrY0ho0NDdnxtD26jG0tGX2bet8B4Hl3Y1LF/Dht+Q/KLo6Ru38X0t3dbor7+r/hrT6r+2UdA1wTbJaL2l9V/Vy8b/7umNuJgKvDO5bDDke8/D3ehsvDNExX3UTXNX33WflUimXoKgFZmatzwB2dFOnVlIRMBbY18O+XZW/AoyTVJQcVXT1XgBExO3A7Tn0P68kVUfE4nz340TymIe/19t44fU55g65HOc+AVRJmiOphMzF6ZWd6qzkeKhdBjwcEZGUL5NUmtzNVAU83l2byT6PJG2QtPnTvg/PzMz6q8cjiuSaw7XAKjK3st4VEWsl3QhUR8RK4E7g7uRi9T4y//CT1LuXzIXvVuDjEdEG0FWbyVv+d2CFpH8Enk7aNjOzPFGEL04NJknXJKfJXjc85uHv9TZeeH2OuYODwszMUvlLA2ZmlspBMYgkXSRpvaQaSdfluz8DQdJMSY9IWidpraRPJuXjJf1S0svJz4qkXJK+mfwO1kg6J78j6DtJhZKelvSzZH2OpNXJmO9JbswguXnjnmTMqyXNzme/+0LSOEn3SXox+azfMtw/Y0mfTv6bfl7SDyWVDefPuDccFIMka+qTi4H5wJXJlCYnu1bgsxFxBvBm4OPJuK4DHoqIKuChZB0y469KXtcwiNOxnACfBNZlrXdMN1MF7Ccz3QxkTWkD3JLUO9l8A3ggIt4ALCQz7mH7GUuaDnwCWBwRZ5K5yaZjOqLh+hnnLvNVer8G+gW8BViVtX49cH2++zUI4/wpmTm71gNTk7KpwPpk+dvAlVn1j9U7mV5kvtPzEJkpZn5G5suhrwBFnT9vMnfzvSVZLkrqKd9j6MVYxwCbOvd5OH/GHJ9dYnzymf0MePdw/Yx7+/IRxeDpauqTQZuOJB+Sw+2zgdXA5IjYCZD8nJRUGy6/h68Dfw90TKaUNt3Mq6a0ATqmtDlZnArUAf+anGq7Q9JIhvFnHBHbgZuBrcBOMp/Zkwzfz7hXHBSDJ+fpSE5GkkYBPwI+FRGH0qp2UXZS/R4kvRfYExFPZhd3UTVy2HYyKALOAb4VEWcDRzh+mqkrJ/t4Sa63LAXmkJnpeiSZU2qdDZfPuFccFIMnl6lPTkqSismExPcj4sdJ8W5JU5PtU4E9Sflw+D28DbhU0mYyz0p5J5kjjHHJlDXw6nEdG3OnKW1OFrVAbUSsTtbvIxMcw/kzfhewKSLqIqIF+DHwVobvZ9wrDorBk8vUJycdSSLzbfl1EfFPWZuyp3HJnnplJfDh5M6YNwMHO05fnCwi4vqImBERs8l8jg9HxAfpfrqZ7qa0OSlExC5gm6TTk6IlZGZXGLafMZlTTm+WNCL5b7xjzMPyM+61fF8kGc4v4BLgJWAD8A/57s8AjelPyRxirwGeSV6XkDk/+xDwcvJzfFJfZO7+2gA8R+aukryPox/j/3PgZ8nyqWTmLqsB/g0oTcrLkvWaZPup+e53H8Z5FlCdfM7/DlQM98+YzETSLwLPA3cDpcP5M+7Ny9/MNjOzVD71ZGZmqRwUZmaWykFhZmapHBRmZpbKQWFmZqkcFGZ5IOnXkv4mWf6gpAfz3Sez7jgozLogabOkd52I94qI70fEhSfivcz6wkFhZmapHBRmKSR9RNKjkm6WtF/SJkkXd9q+UdLhZNsHk/IbJP2/rHqzJUXWvEGveY+s9ZD0d8nDcvZLui2ZVsIsLxwUZj07j8wzFiYCXwHuTOY1Ggl8E7g4IkaTmUTumQF6z/cCf0LmoUF/TebZCGZ54aAw69mWiPhORLQBy8k8tGdysq0dOFNSeUTsjIi1A/SeX46IAxGxlczEdGcNULtmveagMOvZro6FiGhIFkdFxBHgCuDvgJ2Sfi7pDQP9nkADMGqA2jXrNQeFWT9ExKqIuIDMUcaLwHeSTUeAEVlVp5zovpkNFAeFWR9Jmizp0uRaRRNQD7Qlm58Bzpd0iqSxZJ6ZbnZSclCY9V0B8FkyTz3bB7wd+C8AEfFL4B4yz3N4EvhZnvpo1m9+HoWZmaXyEYWZmaVyUJiZWSoHhZmZpXJQmJlZKgeFmZmlclCYmVkqB4WZmaVyUJiZWSoHhZmZpfr/OJEJmMelcBQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Insulin.values, bins=30,kde=True)\n",
    "plt.xlabel('Insulin',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#剩余其他列特征的分布情况，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'number of times')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGllJREFUeJzt3X+UHGWd7/H3ZxNAAmIITDAmwQGMKHoRwlw2inKRsECySEBkhUWNEM3hhwSX6wrceBbdq2dFV/HirmjWRKNiAPkhQRHIjbDolaBDIL8MP7L8CAOBDOsCahBI+N4/6hnTJJWZSldXdzPzeZ3Tp7uervrWN5nu+c7zVNVTigjMzMy29BetTsDMzNqTC4SZmeVygTAzs1wuEGZmlssFwszMcrlAmJlZLhcIMzPL5QJhZma5KisQkuZJWi9pZc57n5IUkvZMy5J0maQ1kpZLmlhVXmZmVszwCmN/F/gX4Hu1jZLGA38FrK1pngJMSI+/BC5Pz/3ac889o7OzszHZmpkNEXfffffTEdEx0HqVFYiIuENSZ85blwKfBm6oaZsGfC+yeT+WSBopaUxErOtvH52dnXR3dzcqZTOzIUHSo0XWa+oxCEnHA49HxLIt3hoLPFaz3JPazMysRaocYnoFSSOA2cDReW/ntOXOIihpJjATYO+9925YfmZm9krN7EHsB+wDLJP0CDAOWCrp9WQ9hvE1644DnsgLEhFzIqIrIro6OgYcQjMzszo1rUBExIqIGB0RnRHRSVYUJkbEk8BC4CPpbKZJwLMDHX8wM7NqVXma6wLgTmB/ST2SZvSz+k3AQ8Aa4N+As6vKy8zMiqnyLKZTB3i/s+Z1AOdUlYuZmW0/X0ltZma5XCDMzCyXC4SZmeVq2nUQrybrvjG71PZjzv5CgzIxM2sd9yDMzCyXC4SZmeVygTAzs1wuEGZmlssFwszMcrlAmJlZLhcIMzPL5QJhZma5XCDMzCyXC4SZmeVygTAzs1wuEGZmlssFwszMcrlAmJlZLhcIMzPL5QJhZma5XCDMzCyXC4SZmeWqrEBImidpvaSVNW1flnSfpOWSrpc0sua9iyStkXS/pGOqysvMzIqpsgfxXeDYLdoWAW+PiAOBB4CLACQdAJwCvC1t8w1JwyrMzczMBlBZgYiIO4DfbdF2a0RsTItLgHHp9TTgyoh4ISIeBtYAh1aVm5mZDayVxyDOAH6WXo8FHqt5rye1mZlZi7SkQEiaDWwEruhrylkttrHtTEndkrp7e3urStHMbMhreoGQNB04DjgtIvqKQA8wvma1ccATedtHxJyI6IqIro6OjmqTNTMbwppaICQdC1wAHB8RG2reWgicImknSfsAE4BfNzM3MzN7peFVBZa0ADgC2FNSD3Ax2VlLOwGLJAEsiYgzI2KVpKuB35INPZ0TEZuqys3MzAZWWYGIiFNzmuf2s/4XgC9UlY+ZmW0fX0ltZma5XCDMzCyXC4SZmeVygTAzs1wuEGZmlssFwszMcrlAmJlZLhcIMzPL5QJhZma5KruSupl6L/9Bqe07zvpQgzIxMxs83IMwM7NcLhBmZpbLBcLMzHK5QJiZWS4XCDMzy+UCYWZmuVwgzMwslwuEmZnlcoEwM7NcLhBmZpbLBcLMzHINirmY2t0933xf3dsefOaNDczEzKy4ynoQkuZJWi9pZU3bKEmLJD2YnndP7ZJ0maQ1kpZLmlhVXmZmVkyVQ0zfBY7dou1CYHFETAAWp2WAKcCE9JgJXF5hXmZmVkBlBSIi7gB+t0XzNGB+ej0fOKGm/XuRWQKMlDSmqtzMzGxgzT5IvVdErANIz6NT+1jgsZr1elLbViTNlNQtqbu3t7fSZM3MhrJ2OYtJOW2Rt2JEzImIrojo6ujoqDgtM7Ohq9kF4qm+oaP0vD619wDja9YbBzzR5NzMzKxGswvEQmB6ej0duKGm/SPpbKZJwLN9Q1FmZtYalV0HIWkBcASwp6Qe4GLgi8DVkmYAa4GT0+o3AVOBNcAG4PSq8jIzs2IqKxARceo23pqcs24A51SVi5mZbb8Bh5gkfUnSbpJ2kLRY0tOSPtSM5MzMrHWKHIM4OiKeA44jO5j8ZuDvK83KzMxarkiB2CE9TwUWRMSWF7+ZmdkgVOQYxI2S7gOeB86W1AH8qdq0zMys1QbsQUTEhcA7ga6IeInsLKNpVSdmZmatVeQg9QiyM4z6JtB7A9BVZVJmZtZ6RY5BfAd4EXhXWu4BPl9ZRmZm1haKFIj9IuJLwEsAEfE8+XMnmZnZIFKkQLwoaWfS5HmS9gNeqDQrMzNruSJnMV0M3AyMl3QFcBjw0SqTMjOz1huwQETEIklLgUlkQ0vnRcTTlWdmZmYtVXQ217HAMGBH4HBJ768uJTMzawcD9iAkzQMOBFYBL6fmAK6rMC8zM2uxIscgJkXEAZVnYmZmbaXIENOdklwgzMyGmCI9iPlkReJJstNbRXYLhwMrzczMzFqqSIGYB3wYWMHmYxBmZjbIFSkQayNiYeWZmJlZWylSIO6T9EPgRmquoI4In8VkZjaIFSkQO5MVhqNr2nyaq5nZIFfkSurTm5GImZm1l20WCEmfjogvSfo6aaK+WhExq96dSvo74GMp7grgdGAMcCUwClgKfDgiXqx3H2ZmVk5/PYjV6bm7kTuUNBaYBRwQEc9Luho4heye15dGxJWSvgnMYPNNiszMrMm2WSAi4sb0ckNE/Kj2PUknN2C/O0t6CRgBrAOOBP42vT8f+CwuEGZmLVPkSuqLCrYVEhGPA/8MrCUrDM8CdwPPRMTGtFoP2QSBZmbWIv0dg5hCNuwzVtJlNW/tBmzM32pgknYHpgH7AM8APwKm5Ky61XGPtP1MYCbA3nvvXW8aZmY2gP56EE+QHX/4E9lf+H2PhcAxJfZ5FPBwRPRGxEtkp8u+Cxgpqa9gjUv730pEzImIrojo6ujoKJGGmZn1p79jEMuAZZJ+mH6RN8paYJKkEcDzwGSyQnQb8AGyM5mmAzc0cJ9mZradBjwG0eDiQETcBVxDdirripTDHOAC4HxJa4A9gLmN3K+ZmW2fIldSN1xEXEx2r+taDwGHtiAdMzPLsc0ehKTvp+fzmpeOmZm1i/6GmA6R9EbgDEm7SxpV+2hWgmZm1hr9DTF9E7gZ2Jfs7CXVvBep3czMBqn+zmK6DLhM0uURcVYTc7J+3DR3aqntp864qUGZmNlgV2Q217MkvQN4T2q6IyKWV5uWmZm12oCnuUqaBVwBjE6PKySdW3ViZmbWWkVOc/0Y8JcR8UcASZcAdwJfrzIxMzNrrSKT9QnYVLO8iVcesDYzs0GoSA/iO8Bdkq5Pyyfgq5zNzAa9IgepvyrpduDdZD2H0yPinqoTMzOz1io01UZELCWbO8nMzIaIIscgzMxsCHKBMDOzXP0OMUkaBtwSEUc1KR9rsnnzjy61/RnTb21QJmbWbvrtQUTEJmCDpNc1KR8zM2sTRQ5S/wlYIWkR8Me+xoiYVVlWZmbWckUKxE/Tw8zMhpAi10HMl7QzsHdE3N+EnMzMrA0UmazvfcC9ZPeGQNJBkhZWnZiZmbVWkdNcP0t2r+hnACLiXmCfCnMyM7M2UKRAbIyIZ7doiyqSMTOz9lHkIPVKSX8LDJM0AZgF/KratMzMrNWK9CDOBd4GvAAsAJ4DPllmp5JGSrpG0n2SVkt6p6RRkhZJejA9715mH2ZmVs6ABSIiNkTEbGAy8N6ImB0Rfyq53/8D3BwRbwHeAawGLgQWR8QEYHFaNjOzFilyFtN/l7QCWE52wdwySYfUu0NJuwGHk+4pEREvRsQzwDRgflptPtl9J8zMrEWKDDHNBc6OiM6I6ATOIbuJUL32BXqB70i6R9K3Je0C7BUR6wDS8+gS+zAzs5KKFIjfR8Qv+hYi4pfA70vsczgwEbg8Ig4mm76j8HCSpJmSuiV19/b2lkjDzMz6s80CIWmipInAryV9S9IRkv6HpG8At5fYZw/QExF3peVryArGU5LGpH2PAdbnbRwRcyKiKyK6Ojo6SqRhZmb96e80169ssXxxzeu6r4OIiCclPSZp/zR1x2Tgt+kxHfhier6h3n2YmVl52ywQEfHeCvd7LnCFpB2Bh4DTyXozV0uaAawFTq5w/2ZmNoABL5STNBL4CNBZu36Z6b7TdB1dOW9NrjemtYfPX3VM3dt+5oO3NDATMyuryJXUNwFLgBXAy9WmY2Zm7aJIgXhNRJxfeSZmZtZWipzm+n1JH5c0Jk2HMUrSqMozMzOzlirSg3gR+DIwm81nLwXZBW9mZjZIFSkQ5wNvioinq07GzMzaR5EhplXAhqoTMTOz9lKkB7EJuFfSbWRTfgPlTnM1M7P2V6RA/Dg9zMxsCBmwQETE/IHWMTOzwafIldQPkzP3UkT4LCYzs0GsyBBT7ZQYryGbI8nXQVjlptxwaqntfzZtQYMyMRuaitxy9D9rHo9HxNeAI5uQm5mZtVCRIaaJNYt/QdajeG1lGZmZWVsoMsRUe1+IjcAjwN9Uko2ZmbWNImcxVXlfCDMza1NFhph2Ak5i6/tB/GN1aZmZWasVGWK6AXgWuJuaK6nNzGxwK1IgxkXEsZVnYmZmbaVIgfiVpP8WESsqz8asQlOvv6TU9jedeEGDMjF7dShSIN4NfDRdUf0CICAi4sBKMzMzs5YqUiCmVJ6FmZm1nSKnuT7ajETMzKy9FLlhUCUkDZN0j6SfpOV9JN0l6UFJV0nasVW5mZlZCwsEcB6wumb5EuDSiJgA/BcwoyVZmZkZ0KICIWkc8NfAt9OyyCYAvCatMh84oRW5mZlZplU9iK8BnwZeTst7AM9ExMa03AOMzdtQ0kxJ3ZK6e3t7q8/UzGyIanqBkHQcsD4i7q5tzll1q5sUAUTEnIjoioiujo6OSnI0M7Nip7k22mHA8ZKmkt2AaDeyHsVIScNTL2Ic8EQLcjMzs6TpPYiIuCgixkVEJ3AK8POIOA24DfhAWm062RxQZmbWIq3oQWzLBcCVkj4P3APMbXE+Zv067tr6P6I/Ockn6Vn7a2mBiIjbgdvT64eAQ1uZj5mZbdbK6yDMzKyNuUCYmVkuFwgzM8vlAmFmZrlcIMzMLJcLhJmZ5XKBMDOzXC4QZmaWywXCzMxyuUCYmVkuFwgzM8vlAmFmZrlcIMzMLFc7TfdtNmS975prS21/4wdOalAmZpu5B2FmZrlcIMzMLJcLhJmZ5XKBMDOzXC4QZmaWy2cxmQ1CJ157W6ntrz/pvQ3KxF7N3IMwM7NcTS8QksZLuk3SakmrJJ2X2kdJWiTpwfS8e7NzMzOzzVrRg9gI/M+IeCswCThH0gHAhcDiiJgALE7LZmbWIk0vEBGxLiKWpte/B1YDY4FpwPy02nzghGbnZmZmm7X0GISkTuBg4C5gr4hYB1kRAUa3LjMzM2tZgZC0K3At8MmIeG47tpspqVtSd29vb3UJmpkNcS0pEJJ2ICsOV0TEdan5KUlj0vtjgPV520bEnIjoioiujo6O5iRsZjYEteIsJgFzgdUR8dWatxYC09Pr6cANzc7NzMw2a8WFcocBHwZWSLo3tf0v4IvA1ZJmAGuBk1uQm5mZJU0vEBHxS0DbeHtyM3MxM7Nt85XUZmaWywXCzMxyuUCYmVkuFwgzM8vlAmFmZrlcIMzMLJcLhJmZ5XKBMDOzXC4QZmaWy/ekNrMBffDaB0ptf9VJb25QJtZMLhBm9qr28yvKTft/5GmeFXpbPMRkZma53IMws6aac13urV4Km/l+32yyWdyDMDOzXC4QZmaWywXCzMxyuUCYmVkuFwgzM8vlAmFmZrlcIMzMLJcLhJmZ5XKBMDOzXG1XICQdK+l+SWskXdjqfMzMhqq2mmpD0jDgX4G/AnqA30haGBG/bW1mZjZUPPgvT5XafsIn9mpQJq3XVgUCOBRYExEPAUi6EpgGuECY2avSk19dVfe2rz//ba9YXv/1xaVyGX3u5O1av92GmMYCj9Us96Q2MzNrMkVEq3P4M0knA8dExMfS8oeBQyPi3Jp1ZgIz0+L+wP0FQu8JPN3AVBsZr51za/d47Zxbo+O1c26NjtfOuTU6Xqtye2NEDHgjjHYbYuoBxtcsjwOeqF0hIuYAc7YnqKTuiOgqn17j47Vzbu0er51za3S8ds6t0fHaObdGx2vn3KD9hph+A0yQtI+kHYFTgIUtzsnMbEhqqx5ERGyU9AngFmAYMC8i6j/CY2ZmdWurAgEQETcBNzU47HYNSTU5Xjvn1u7x2jm3Rsdr59waHa+dc2t0vHbOrb0OUpuZWftot2MQZmbWJgZ9gWjk1B2S5klaL2llA/IaL+k2SaslrZJ0Xsl4r5H0a0nLUrzPNSDHYZLukfSTBsR6RNIKSfdK6m5AvJGSrpF0X/o/fGeJWPunvPoez0n6ZIl4f5d+BislLZD0mnpjpXjnpVir6skr73MraZSkRZIeTM+7l4h1csrtZUnbdQbNNuJ9Of1cl0u6XtLIkvH+d4p1r6RbJb2hTLya9z4lKSTtWSK3z0p6vOazN7VMbpKuqon1iKR7i8bLFRGD9kF2oPs/gH2BHYFlwAEl4h0OTARWNiC3McDE9Pq1wAMlcxOwa3q9A3AXMKlkjucDPwR+0oB/7yPAng382c4HPpZe7wiMbOBn5kmy88Tr2X4s8DCwc1q+GvhoiXzeDqwERpAdM/y/wITtjLHV5xb4EnBhen0hcEmJWG8luybpdqCrAbkdDQxPry8pmls/8XareT0L+GaZeKl9PNnJNI8W/VxvI7fPAp+q87PR7+8j4CvAP9T72YuIQd+D+PPUHRHxItA3dUddIuIO4HeNSCwi1kXE0vT698BqSlw1Hpk/pMUd0qPuA0ySxgF/DXy73hhVkbQb2ZdjLkBEvBgRzzQo/GTgPyLi0RIxhgM7SxpO9ov9iQHW789bgSURsSEiNgL/Dpy4PQG28bmdRlZkSc8n1BsrIlZHRJELVovGuzX9WwGWkF0PVSbeczWLu7Ad34t+vvOXAp9uUKy69BdPkoC/ARaU2cdgLxCviqk7JHUCB5P91V8mzrDUpVwPLIqIMvG+RvYFeLlMTjUCuFXS3elq+DL2BXqB76QhsG9L2qV8ikB27U3dX6qIeBz4Z2AtsA54NiJuLZHPSuBwSXtIGgFM5ZUXk9Zrr4hYB9kfK8DoBsSswhnAz8oGkfQFSY8BpwH/UDLW8cDjEbGsbF7JJ9IQ2LyiQ30FvAd4KiIeLBNksBcI5bS11WlbknYFrgU+ucVfOtstIjZFxEFkf3EdKuntdeZ0HLA+Iu4uk88WDouIicAU4BxJh5eINZysa315RBwM/JFsmKSUdHHm8cCPSsTYneyv832ANwC7SPpQvfEiYjXZMMsi4GayYdKN/W40SEiaTfZvvaJsrIiYHRHjU6xPlMhpBDCbkkWmxuXAfsBBZH9QfKVBcU+lZO8BBn+BGHDqjlaStANZcbgiIq5rVNw03HI7cGydIQ4Djpf0CNmw3JGSflAypyfS83rgerLhv3r1AD01PaRryApGWVOApRFRZr7no4CHI6I3Il4CrgPeVSapiJgbERMj4nCyIYVSfxUmT0kaA5Ce1zcgZsNImg4cB5wWaUC9QX4InFRi+/3Iiv+y9P0YByyV9Pp6gkXEU+kPu5eBf6Pc9wKANLT5fuCqsrEGe4Fo26k70hjhXGB1RHy1AfE6+s72kLQz2S+q++qJFREXRcS4iOgk+z/7eUTU/VewpF0kvbbvNdlByLrPBIuIJ4HHJO2fmibTmCnhG/FX11pgkqQR6Wc8mez4Ut0kjU7Pe5N98Uv/ZUj2PZieXk8HbmhAzIaQdCxwAXB8RGxoQLwJNYvHU+f3AiAiVkTE6IjoTN+PHrKTTZ6sM7cxNYsnUuJ7UeMo4L6I6CkdqcwR7lfDg2zM9gGys5lml4y1gKwb+BLZB2NGiVjvJhvuWg7cmx5TS8Q7ELgnxVtJybMXauIeQcmzmMiOGSxLj1Vlfw4p5kFAd/r3/hjYvWS8EcB/Aq9rQG6fI/sltBL4PrBTyXi/ICuAy4DJdWy/1ecW2ANYTNYbWQyMKhHrxPT6BeAp4JaSua0hO3bY973YnrOO8uJdm34Wy4EbgbFl4m3x/iMUP4spL7fvAytSbguBMWVzA74LnFn2cxwRvpLazMzyDfYhJjMzq5MLhJmZ5XKBMDOzXC4QZmaWywXCzMxyuUDYoCdpU5rdcqWkH6WrYV8VJP2q1TnY0OUCYUPB8xFxUES8HXgROLP2TWXa8rsQEaWuwjYroy2/FGYV+gXwJkmdyu4j8Q1gKTBe0tGS7pS0NPU0dgWQNDXdn+CXki5Tuj9Gmst/nqTbJT0kaVbfTiT9OE1MuKp2ckJJf0gTxy2TtETSXql9L2X3PliWHu/qW79m27+X9Js0sdvnUtsukn6atlkp6YNN+D+0IcIFwoaMNEfNFLIrVyG7h8H3YvOEf58BjopsUsFu4HxlN/v5FjAlIt4NdGwR9i3AMWRz6Fyc5tcCOCMiDgG6gFmS9kjtu5BN3/0O4A7g46n9MuDfU/tEsivOa3M/GpiQ9nMQcEia8PBY4ImIeEfqId1c//+Q2Su5QNhQsHOaBr2bbK6kuan90YhYkl5PAg4A/l9adzrwRrIC8FBEPJzW23IepJ9GxAsR8TTZhHd7pfZZkpaR3c9gPNkvd8iGuPru0Hc30JleH0k2syeRTd727Bb7OTo97iHr8bwlxVwBHCXpEknvydnOrG7DW52AWRM8H9k06H+WzaPHH2ubyO6hceoW6x08QOwXal5vAoZLOoJswrR3RsQGSbcDfbcdfSk2z2+zieLfQQH/FBHf2uoN6RCyOcf+SdKtEfGPBWOa9cs9CLPMEuAwSW+CbN5/SW8mm3Rv33RTJ4AiY/yvA/4rFYe3kPVOBrIYOCvte5iyu+bVugU4o+a4yFhJo5XdX3lDRPyA7EZFjZj23AxwD8IMgIjolfRRYIGknVLzZyLiAUlnAzdLehr4dYFwNwNnSloO3E9WfAZyHjBH0gyynsVZwJ01+d0q6a3Anan38wfgQ8CbgC9LeplsVs+zCuzLrBDP5mo2AEm7RsQf0v0d/hV4MCIubXVeZlXzEJPZwD6eDlyvIhs+2uo4gNlg5B6EmZnlcg/CzMxyuUCYmVkuFwgzM8vlAmFmZrlcIMzMLJcLhJmZ5fr/ZeiejMzofMMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.countplot(train.Pregnancies)\n",
    "plt.xlabel('Pregnancies')\n",
    "plt.ylabel('number of times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.DiabetesPedigreeFunction.values, bins=30,kde=True)\n",
    "plt.xlabel('DiabetesPedigreeFunction',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Age.values, bins=30,kde=True)\n",
    "plt.xlabel('Age',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看特征和特征之间的相关性\n",
    "\n",
    "cols=train.columns\n",
    "train_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(16, 10))\n",
    "sns.heatmap(train_corr,annot=True)\n",
    "\n",
    "sns.heatmap(train_corr, mask=train_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('diabetes_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18bcee781d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#看看特征和患病概率的分布。\n",
    "sns.countplot(x = \"Pregnancies\" , hue = \"Outcome\" , data = train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18bcefadef0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"Glucose\" , hue = \"Outcome\" , data = train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18bcf0d5c18>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"BMI\" , hue = \"Outcome\" , data = train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18bcec79e48>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"Age\" , hue = \"Outcome\" , data = train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   0\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     374\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#进行缺失值的处理，先把这些数值为零的数据标记为空值，然后查看每一列的缺失个数。并进行填补。\n",
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "train [ NaN_col_names ] = train [ NaN_col_names ].replace ( 0,np.NAN )\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#先使用中位数进行填补\n",
    "\n",
    "mean = train.mean()\n",
    "train = train.fillna(mean)\n",
    "\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存结果\n",
    "train.to_csv('FE_diabetes.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
